Mon Nov 04 00:00:00 UTC 2024: ## New Model Predicts Newborn Weight with Routine Scans

**Chennai, India** – Researchers from Chennai and Pune have developed a model that can predict a newborn’s birth weight using routine scans during pregnancy, potentially eliminating the need for late-term ultrasounds. The model, based on the Gompertz formula, uses data from at least three routine scans to estimate foetal measurements at birth, leading to a reliable prediction of the baby’s weight.

The research, published in the European Journal of Obstetrics & Gynecology and Reproductive Biology: X, marks a significant advancement in prenatal care. Accurate birth weight prediction is crucial for identifying potential risks and planning interventions, as low weight can lead to complications like preterm birth, while heavier babies may pose challenges during delivery.

“This is a very interesting and important study,” said Tavpritesh Sethi, a computational biologist at the Indraprastha Institute of Information Technology, Delhi. “If the model is validated across bigger settings, it essentially reduces the need for carrying out a lot of ultrasounds.”

The researchers tested their model on data from over 750 pregnant women in South India and found it predicted more than 70% of birth weights with an error margin of less than 10%. Further validation with a geographically and ethnically diverse population is needed, as well as incorporating the nutritional status of pregnant women, which can influence foetal growth.

The team has published the code for their prediction software online and plans to make an easy and free online calculator for clinicians. They also hope to integrate the formula into ultrasound machine software, making it readily available to healthcare providers.

While the model shows promise, the researchers emphasize the need for data sharing in healthcare to improve its accuracy and applicability across diverse populations. “One thing the community is lacking is … that we don’t have a lot of publicly available datasets,” said Leelavati Narlikar, one of the researchers. “It would be a great thing if we had more of them.”

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